Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions

PLoS One. 2018 Oct 11;13(10):e0205152. doi: 10.1371/journal.pone.0205152. eCollection 2018.

Abstract

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Agriculture / legislation & jurisprudence
  • Computer Simulation*
  • Crops, Agricultural*
  • Environment
  • Glycine max
  • Models, Econometric*
  • Software
  • Statistics as Topic
  • Triticum
  • Zea mays

Grants and funding

This research is supported in part by USDA grant agreement #58300010058, Purdue grant #105651; computational resources provided by Information Technology at Purdue – the Carter Cluster, Purdue University, West Lafayette, Indiana. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.